{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# actions: hit or stand\n",
    "ACTION_HIT = 0\n",
    "ACTION_STAND = 1  #  \"strike\" in the book\n",
    "ACTIONS = [ACTION_HIT, ACTION_STAND]\n",
    "# policy for player\n",
    "POLICY_PLAYER = np.zeros(22, dtype=np.int32)\n",
    "for i in range(12, 20):\n",
    "    POLICY_PLAYER[i] = ACTION_HIT\n",
    "POLICY_PLAYER[20] = ACTION_STAND\n",
    "POLICY_PLAYER[21] = ACTION_STAND\n",
    "\n",
    "# function form of target policy of player\n",
    "def target_policy_player(usable_ace_player, player_sum, dealer_card):\n",
    "    return POLICY_PLAYER[player_sum]\n",
    "\n",
    "# function form of behavior policy of player\n",
    "def behavior_policy_player(usable_ace_player, player_sum, dealer_card):\n",
    "    if np.random.binomial(1, 0.5) == 1:\n",
    "        return ACTION_STAND\n",
    "    return ACTION_HIT\n",
    "\n",
    "# policy for dealer\n",
    "POLICY_DEALER = np.zeros(22)\n",
    "for i in range(12, 17):\n",
    "    POLICY_DEALER[i] = ACTION_HIT\n",
    "for i in range(17, 22):\n",
    "    POLICY_DEALER[i] = ACTION_STAND\n",
    "\n",
    "# get a new card\n",
    "def get_card():\n",
    "    card = np.random.randint(1, 14)\n",
    "    card = min(card, 10)\n",
    "    return card\n",
    "\n",
    "# get the value of a card (11 for ace).\n",
    "def card_value(card_id):\n",
    "    return 11 if card_id == 1 else card_id\n",
    "\n",
    "\n",
    "\n",
    "# play a game\n",
    "# @policy_player: specify policy for player\n",
    "# @initial_state: [whether player has a usable Ace, sum of player's cards, one card of dealer]\n",
    "# @initial_action: the initial action\n",
    "\n",
    "def play(policy_player, initial_state=None, initial_action=None):\n",
    "    # player status\n",
    "\n",
    "    # sum of player\n",
    "    player_sum = 0\n",
    "\n",
    "    # trajectory of player\n",
    "    player_trajectory = []\n",
    "\n",
    "    # whether player uses Ace as 11\n",
    "    usable_ace_player = False\n",
    "\n",
    "    # dealer status\n",
    "    dealer_card1 = 0\n",
    "    dealer_card2 = 0\n",
    "    usable_ace_dealer = False\n",
    "    if initial_state is None:\n",
    "        # generate a random initial state\n",
    "        while player_sum < 12:\n",
    "            # if sum of player is less than 12, always hit\n",
    "            card = get_card()\n",
    "            player_sum += card_value(card)\n",
    "\n",
    "            # If the player's sum is larger than 21, he may hold one or two aces.\n",
    "            if player_sum > 21:\n",
    "                assert player_sum == 22\n",
    "                # last card must be ace\n",
    "                player_sum -= 10\n",
    "            else:\n",
    "                usable_ace_player |= (1 == card)\n",
    "        # initialize cards of dealer, suppose dealer will show the first card he gets\n",
    "        dealer_card1 = get_card()\n",
    "        dealer_card2 = get_card()\n",
    "    else:\n",
    "        # use specified initial state\n",
    "        usable_ace_player, player_sum, dealer_card1 = initial_state\n",
    "        dealer_card2 = get_card()\n",
    "\n",
    "    # initial state of the game\n",
    "    state = [usable_ace_player, player_sum, dealer_card1]\n",
    "\n",
    "    # initialize dealer's sum\n",
    "    dealer_sum = card_value(dealer_card1) + card_value(dealer_card2)\n",
    "    usable_ace_dealer = 1 in (dealer_card1, dealer_card2)\n",
    "    # if the dealer's sum is larger than 21, he must hold two aces.\n",
    "    if dealer_sum > 21:\n",
    "        assert dealer_sum == 22\n",
    "        # use one Ace as 1 rather than 11\n",
    "        dealer_sum -= 10\n",
    "    assert dealer_sum <= 21\n",
    "    assert player_sum <= 21\n",
    "\n",
    "# game starts!\n",
    "\n",
    "    # player's turn\n",
    "    while True:\n",
    "        if initial_action is not None:\n",
    "            action = initial_action\n",
    "            initial_action = None\n",
    "        else:\n",
    "            # get action based on current sum\n",
    "            action = policy_player(usable_ace_player, player_sum, dealer_card1)\n",
    "\n",
    "        # track player's trajectory for importance sampling\n",
    "        player_trajectory.append([(usable_ace_player, player_sum, dealer_card1), action])\n",
    "        if action == ACTION_STAND:\n",
    "            break\n",
    "        card = get_card()\n",
    "        # Keep track of the ace count. the usable_ace_player flag is insufficient alone as it cannot\n",
    "        # distinguish between having one ace or two.\n",
    "        ace_count = int(usable_ace_player)\n",
    "        if card == 1:\n",
    "            ace_count += 1\n",
    "\n",
    "        player_sum += card_value(card)\n",
    "        # If the player has a usable ace, use it as 1 to avoid busting and continue.\n",
    "        while player_sum > 21 and ace_count:\n",
    "            player_sum -= 10\n",
    "            ace_count -= 1\n",
    "        # player busts\n",
    "        if player_sum > 21:\n",
    "            return state, -1, player_trajectory\n",
    "        assert player_sum <= 21\n",
    "        usable_ace_player = (ace_count == 1)\n",
    "# dealer's turn\n",
    "    while True:\n",
    "        # get action based on current sum\n",
    "        action = POLICY_DEALER[dealer_sum]\n",
    "        if action == ACTION_STAND:\n",
    "            break\n",
    "        # if hit, get a new card\n",
    "        new_card = get_card()\n",
    "        ace_count = int(usable_ace_dealer)\n",
    "        if new_card == 1:\n",
    "            ace_count += 1\n",
    "        dealer_sum += card_value(new_card)\n",
    "        # If the dealer has a usable ace, use it as 1 to avoid busting and continue.\n",
    "        while dealer_sum > 21 and ace_count:\n",
    "            dealer_sum -= 10\n",
    "            ace_count -= 1\n",
    "        # dealer busts\n",
    "        if dealer_sum > 21:\n",
    "            return state, 1, player_trajectory\n",
    "        usable_ace_dealer = (ace_count == 1)\n",
    "\n",
    "    # compare the sum between player and dealer\n",
    "    assert player_sum <= 21 and dealer_sum <= 21\n",
    "    if player_sum > dealer_sum:\n",
    "        return state, 1, player_trajectory\n",
    "    elif player_sum == dealer_sum:\n",
    "        return state, 0, player_trajectory\n",
    "    else:\n",
    "        return state, -1, player_trajectory\n",
    "\n",
    "# Monte Carlo Sample with On-Policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Monte Carlo Sample with On-Policy\n",
    "def monte_carlo_on_policy(episodes):\n",
    "    states_usable_ace = np.zeros((10, 10))\n",
    "    # initialze counts to 1 to avoid 0 being divided\n",
    "    states_usable_ace_count = np.ones((10, 10))\n",
    "    states_no_usable_ace = np.zeros((10, 10))\n",
    "    # initialze counts to 1 to avoid 0 being divided\n",
    "    states_no_usable_ace_count = np.ones((10, 10))\n",
    "    for i in tqdm(range(0, episodes)):\n",
    "        _, reward, player_trajectory = play(target_policy_player)\n",
    "        for (usable_ace, player_sum, dealer_card), _ in player_trajectory:\n",
    "            player_sum -= 12\n",
    "            dealer_card -= 1\n",
    "            if usable_ace:\n",
    "                states_usable_ace_count[player_sum, dealer_card] += 1\n",
    "                states_usable_ace[player_sum, dealer_card] += reward\n",
    "            else:\n",
    "                states_no_usable_ace_count[player_sum, dealer_card] += 1\n",
    "                states_no_usable_ace[player_sum, dealer_card] += reward\n",
    "    return states_usable_ace / states_usable_ace_count, states_no_usable_ace / states_no_usable_ace_count\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Monte Carlo with Exploring Starts\n",
    "def monte_carlo_es(episodes):\n",
    "    # (playerSum, dealerCard, usableAce, action)\n",
    "    state_action_values = np.zeros((10, 10, 2, 2))\n",
    "    # initialze counts to 1 to avoid division by 0\n",
    "    state_action_pair_count = np.ones((10, 10, 2, 2))\n",
    "\n",
    "    # behavior policy is greedy\n",
    "    def behavior_policy(usable_ace, player_sum, dealer_card):\n",
    "        usable_ace = int(usable_ace)\n",
    "        player_sum -= 12\n",
    "        dealer_card -= 1\n",
    "        # get argmax of the average returns(s, a)\n",
    "        values_ = state_action_values[player_sum, dealer_card, usable_ace, :] / \\\n",
    "                  state_action_pair_count[player_sum, dealer_card, usable_ace, :]\n",
    "        return np.random.choice([action_ for action_, value_ in enumerate(values_) if value_ == np.max(values_)])\n",
    "\n",
    "    # play for several episodes\n",
    "    for episode in tqdm(range(episodes)):\n",
    "        # for each episode, use a randomly initialized state and action\n",
    "        initial_state = [bool(np.random.choice([0, 1])),\n",
    "                       np.random.choice(range(12, 22)),\n",
    "                       np.random.choice(range(1, 11))]\n",
    "        initial_action = np.random.choice(ACTIONS)\n",
    "        current_policy = behavior_policy if episode else target_policy_player\n",
    "        _, reward, trajectory = play(current_policy, initial_state, initial_action)\n",
    "        first_visit_check = set()\n",
    "        for (usable_ace, player_sum, dealer_card), action in trajectory:\n",
    "            usable_ace = int(usable_ace)\n",
    "            player_sum -= 12\n",
    "            dealer_card -= 1\n",
    "            state_action = (usable_ace, player_sum, dealer_card, action)\n",
    "            if state_action in first_visit_check:\n",
    "                continue\n",
    "            first_visit_check.add(state_action)\n",
    "            # update values of state-action pairs\n",
    "            state_action_values[player_sum, dealer_card, usable_ace, action] += reward\n",
    "            state_action_pair_count[player_sum, dealer_card, usable_ace, action] += 1\n",
    "\n",
    "    return state_action_values / state_action_pair_count\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Monte Carlo Sample with Off-Policy\n",
    "def monte_carlo_off_policy(episodes):\n",
    "    initial_state = [True, 13, 2]\n",
    "\n",
    "    rhos = []\n",
    "    returns = []\n",
    "\n",
    "    for i in range(0, episodes):\n",
    "        _, reward, player_trajectory = play(behavior_policy_player, initial_state=initial_state)\n",
    "\n",
    "        # get the importance ratio\n",
    "        numerator = 1.0\n",
    "        denominator = 1.0\n",
    "        for (usable_ace, player_sum, dealer_card), action in player_trajectory:\n",
    "            if action == target_policy_player(usable_ace, player_sum, dealer_card):\n",
    "                denominator *= 0.5\n",
    "            else:\n",
    "                numerator = 0.0\n",
    "                break\n",
    "        rho = numerator / denominator\n",
    "        rhos.append(rho)\n",
    "        returns.append(reward)\n",
    "\n",
    "    rhos = np.asarray(rhos)\n",
    "    returns = np.asarray(returns)\n",
    "    weighted_returns = rhos * returns\n",
    "\n",
    "    weighted_returns = np.add.accumulate(weighted_returns)\n",
    "    rhos = np.add.accumulate(rhos)\n",
    "\n",
    "    ordinary_sampling = weighted_returns / np.arange(1, episodes + 1)\n",
    "\n",
    "    with np.errstate(divide='ignore',invalid='ignore'):\n",
    "        weighted_sampling = np.where(rhos != 0, weighted_returns / rhos, 0)\n",
    "\n",
    "    return ordinary_sampling, weighted_sampling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def figure_5_1():\n",
    "    states_usable_ace_1, states_no_usable_ace_1 = monte_carlo_on_policy(10000)\n",
    "    states_usable_ace_2, states_no_usable_ace_2 = monte_carlo_on_policy(500000)\n",
    "\n",
    "    states = [states_usable_ace_1,\n",
    "              states_usable_ace_2,\n",
    "              states_no_usable_ace_1,\n",
    "              states_no_usable_ace_2]\n",
    "\n",
    "    titles = ['Usable Ace, 10000 Episodes',\n",
    "              'Usable Ace, 500000 Episodes',\n",
    "              'No Usable Ace, 10000 Episodes',\n",
    "              'No Usable Ace, 500000 Episodes']\n",
    "\n",
    "    _, axes = plt.subplots(2, 2, figsize=(40, 30))\n",
    "    plt.subplots_adjust(wspace=0.1, hspace=0.2)\n",
    "    axes = axes.flatten()\n",
    "\n",
    "    for state, title, axis in zip(states, titles, axes):\n",
    "        fig = sns.heatmap(np.flipud(state), cmap=\"YlGnBu\", ax=axis, xticklabels=range(1, 11),\n",
    "                          yticklabels=list(reversed(range(12, 22))))\n",
    "        fig.set_ylabel('player sum', fontsize=30)\n",
    "        fig.set_xlabel('dealer showing', fontsize=30)\n",
    "        fig.set_title(title, fontsize=30)\n",
    "\n",
    "\n",
    "def figure_5_2():\n",
    "    state_action_values = monte_carlo_es(500000)\n",
    "\n",
    "    state_value_no_usable_ace = np.max(state_action_values[:, :, 0, :], axis=-1)\n",
    "    state_value_usable_ace = np.max(state_action_values[:, :, 1, :], axis=-1)\n",
    "\n",
    "    # get the optimal policy\n",
    "    action_no_usable_ace = np.argmax(state_action_values[:, :, 0, :], axis=-1)\n",
    "    action_usable_ace = np.argmax(state_action_values[:, :, 1, :], axis=-1)\n",
    "\n",
    "    images = [action_usable_ace,\n",
    "              state_value_usable_ace,\n",
    "              action_no_usable_ace,\n",
    "              state_value_no_usable_ace]\n",
    "\n",
    "    titles = ['Optimal policy with usable Ace',\n",
    "              'Optimal value with usable Ace',\n",
    "              'Optimal policy without usable Ace',\n",
    "              'Optimal value without usable Ace']\n",
    "\n",
    "    _, axes = plt.subplots(2, 2, figsize=(40, 30))\n",
    "    plt.subplots_adjust(wspace=0.1, hspace=0.2)\n",
    "    axes = axes.flatten()\n",
    "\n",
    "    for image, title, axis in zip(images, titles, axes):\n",
    "        fig = sns.heatmap(np.flipud(image), cmap=\"YlGnBu\", ax=axis, xticklabels=range(1, 11),\n",
    "                          yticklabels=list(reversed(range(12, 22))))\n",
    "        fig.set_ylabel('player sum', fontsize=30)\n",
    "        fig.set_xlabel('dealer showing', fontsize=30)\n",
    "        fig.set_title(title, fontsize=30)\n",
    "\n",
    "\n",
    "def figure_5_3():\n",
    "    true_value = -0.27726\n",
    "    episodes = 10000\n",
    "    runs = 100\n",
    "    error_ordinary = np.zeros(episodes)\n",
    "    error_weighted = np.zeros(episodes)\n",
    "    for i in tqdm(range(0, runs)):\n",
    "        ordinary_sampling_, weighted_sampling_ = monte_carlo_off_policy(episodes)\n",
    "        # get the squared error\n",
    "        error_ordinary += np.power(ordinary_sampling_ - true_value, 2)\n",
    "        error_weighted += np.power(weighted_sampling_ - true_value, 2)\n",
    "    error_ordinary /= runs\n",
    "    error_weighted /= runs\n",
    "\n",
    "    plt.plot(np.arange(1, episodes + 1), error_ordinary, color='green', label='Ordinary Importance Sampling')\n",
    "    plt.plot(np.arange(1, episodes + 1), error_weighted, color='red', label='Weighted Importance Sampling')\n",
    "    plt.ylim(-0.1, 5)\n",
    "    plt.xlabel('Episodes (log scale)')\n",
    "    plt.ylabel(f'Mean square error\\n(average over {runs} runs)')\n",
    "    plt.xscale('log')\n",
    "    plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:00<00:00, 48609.27it/s]\n",
      "100%|██████████| 500000/500000 [00:09<00:00, 51043.58it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4000x3000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure_5_1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500000/500000 [00:38<00:00, 12886.16it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 4000x3000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure_5_2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/100 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:15<00:00,  6.50it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure_5_3()"
   ]
  },
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